CN107729455A - A kind of social network opinion leader sort algorithm based on multidimensional characteristic analysis - Google Patents
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Abstract
The invention discloses a kind of social network opinion leader sort algorithm based on multidimensional characteristic analysis, a：By being analyzed the feature of user and being calculated its characteristic value from three structure, behavior, theme aspects；b：The weight of different characteristic is calculated using entropy assessment；c：S (u), B (u) and T (u) characteristic value and the weight w that b is drawn are drawn with reference to aj, the value of F (u) function is calculated according to MFP algorithms, ranking result is produced by F (u) functional values.A kind of described social network opinion leader sort algorithm based on multidimensional characteristic analysis, considers the multiple features for influenceing diffusion of information, including architectural feature, behavioural characteristic and theme feature；Calculate the weight of its feature respectively using entropy assessment, avoid the influence of subjective judgement, make the ranking of final result more accurate.
Description
Technical field
The present invention relates to field of social network, especially a kind of social network opinion leader row based on multidimensional characteristic analysis
Sequence algorithm.
Background technology
Social networks is the metastable social structure formed by interacting between members of society, has complexity
Network structure and information dynamic communication mechanism.Scale increase and normative enhancing with social networks, it is large-scale at present
Social networks often has user, and actively, between user interactive mode is more more complete than more consistent and user characteristics and behavior record ratio
The characteristics of, this brings great convenience to the analysis and research of social effectiveness.Therefore, it is existing many in social networks at present
Practical application on social networks.Another important application of social influence power：Leader of opinion excavates.
Leader of opinion often refers to the user with larger social effectiveness, how more effectively to analyze the meaning in social networks
See leader, have the function that to social phenomenons such as political election, accident propagation, internet word-of-mouth effects important.Excavate society
Leader of opinion in network, hot news instantly or hot information can be not only obtained, can also promoted each on social networks
The development of kind application, excavate the potential value important role of social networks.
Current stage, the research emphasis on leader of opinion is different, is such as based on network structure, using node in-degree, in
Jie's centrality, close to the features such as centrality come to user carry out ranking；By building social networks network and being based on user behavior
The leader of opinion in community is found with interest worlds；The content either delivered from user, text semantic information is analyzed, dug
The potential emotion of user is dug, and then finds the leader of opinion in community.
It can be seen that by above resolving：These method accuracys are not high, it is impossible to effectively reflect the influence of user
Power, so as to be impacted to final ranking；And few people by these three aspects of bonding behavior, structure and theme come
Solves the problems, such as leader of opinion's ranking.
The content of the invention
The problem to be solved in the present invention is：For ranking accuracy in current leader of opinion's algorithm it is not high the problem of, it is comprehensive
Consider structure, the feature of three aspects of behavior and theme, calculate their weight respectively using entropy assessment, avoid subjective judgement
Influence.
The technical solution adopted for the present invention to solve the technical problems is：A kind of social networks based on multidimensional characteristic analysis
Leader of opinion's sort algorithm, specific method are as follows：
(1) by being analyzed from three structure, behavior, theme aspects the feature of user, wherein architectural feature S
(u) calculation formula is：S (u)=(ufollowing+ureminding+ubetweenness)/3, behavioural characteristic B (u) calculation formula are：B
(u)=(uactivity+uspread)/2, theme feature T (u) calculation formula are：Wherein ufollowing
To follow influence power, uremindingTo refer to influence power, uconnectionTo contact influence power, uactivityTo enliven influence power,
uspreadFor propagating influence, topic (p) is user's theme feature；
(2) the weight w of different characteristic is calculated using entropy assessmentj, calculating process is：
A, the proportion p of j-th of feature under i-th of sample is calculatedij：
Wherein rijThe value of j-th of feature under i-th of sample is expressed as,
B, the comentropy e of j-th of feature is calculatedj：
M indicates m sample in formula,
C, the weight w of j-th of feature is calculatedj：
(3) S (u), B (u) and T (u) characteristic value and the weight w that (2) are drawn are drawn with reference to (1)j, according to MFP algorithms
The value of F (u) function is calculated, ranking result is produced by F (u) functional values, its F (u) calculation formula is：F (u)=w1*S(u)+
w2*B(u)+w3*T(u)。
Described ufollowingFor good friend's number of each user, uremindingFor the bean vermicelli number of each user, uconnectionFor
Intermediary's centrad of each user, uactivityPosted for each user and count, comment on number and forwarding model number, uspreadFor model
Number is forwarded by comment number and model, topic (p) determines by model sum and with the ratio of the cluster belonging to model, i.e.,Wherein CiCluster (being drawn by K-means algorithms) belonging to model, | P | it is model sum.
Entropy assessment algorithm comprises the following steps that：(1) the characteristic development of judgment matrix in social networks；(2) sentence
Disconnected matrix normalization processing, obtains normalizing judgment matrix；(3) the proportion p of each feature is calculatedij；(4) each feature is calculated
Comentropy ej；(5) the weight w of each feature is calculatedj。
MFP algorithms comprise the following steps that：(1) data prediction；(2) analyze and calculate architectural feature S (u), behavioural characteristic B
(u), theme feature T (u) value；(3) the weight w of different characteristic is calculated according to entropy assessmentj；(4) by weight wjAnd S (u), T
(u), B (u) characteristic value substitutes into formula and carries out F (u) iterative calculation；(5) according to F (u) functional values, ranking result is produced.
The beneficial effects of the invention are as follows：A kind of described social network opinion leader based on multidimensional characteristic analysis, which sorts, to be calculated
Method, the algorithm not only analyze multidimensional characteristic, and consider the weight of different characteristic.Multiple features are carried out using entropy assessment
Overall merit, the influence of subjective factor is avoided, make ranking results more accurate.
Brief description of the drawings
The present invention is further described with reference to the accompanying drawings and examples.
Fig. 1 is a kind of framework of social network opinion leader sort algorithm based on multidimensional characteristic analysis of the present invention
Structure chart；
Fig. 2 is a kind of social network opinion leader sort algorithm multiple features based on multidimensional characteristic analysis of the present invention
The hierarchical chart of analysis；
Fig. 3 is a kind of entropy weight of social network opinion leader sort algorithm based on multidimensional characteristic analysis of the present invention
The basic flow sheet of method；
Fig. 4 is a kind of MFP of social network opinion leader sort algorithm based on multidimensional characteristic analysis of the present invention
Calculate the basic flow sheet of algorithm；
Fig. 5 is a kind of correlation of social network opinion leader sort algorithm based on multidimensional characteristic analysis of the present invention
The comparison figure of coefficient baseline algorithm；
Fig. 6 is a kind of the overlapping of social network opinion leader sort algorithm based on multidimensional characteristic analysis of the present invention
The comparison figure of rate baseline algorithm；
Fig. 7 is a kind of algorithm of social network opinion leader sort algorithm based on multidimensional characteristic analysis of the present invention
Step schematic diagram.
Embodiment
In conjunction with the accompanying drawings, the present invention is further explained in detail.These accompanying drawings are simplified schematic diagram, only with
Illustration illustrates the basic structure of the present invention, therefore it only shows the composition relevant with the present invention.
A kind of social network opinion leader sort algorithm based on multidimensional characteristic analysis as shown in Figure 1, specific algorithm is such as
Under：
(1) by being analyzed from three structure, behavior, theme aspects the feature of user, wherein architectural feature S
(u) calculation formula is：S (u)=(ufollowing+ureminding+ubetweenness)/3, behavioural characteristic B (u) calculation formula are：B
(u)=(uactivity+uspread)/2, theme feature T (u) calculation formula are：Wherein ufollowing
To follow influence power, uremindingTo refer to influence power, uconnectionTo contact influence power, uactivityTo enliven influence power,
uspreadFor propagating influence, topic (p) is user's theme feature；
(2) the entropy weight w of different characteristic is calculated using entropy assessmentj, calculating process is：
A, the proportion p of j-th of feature under i-th of sample is calculatedij：
Wherein rijThe value of j-th of feature under i-th of sample is expressed as,
B, the comentropy e of j-th of feature is calculatedj：
M indicates m sample in formula,
C, the weight w of j-th of feature is calculatedj：
The specific steps of entropy assessment are as shown in Figure 3：1. the characteristic development of judgment matrix in social networks, 2. sentences
Disconnected matrix normalization processing, obtains normalizing judgment matrix；3. calculate the proportion p of each featureij；4. calculate the letter of each feature
Cease entropy ej；5. calculate the weight w of each featurej。
(3)：The entropy weight drawn with reference to characteristic value and (2) that (1) is drawn, function F (u) is calculated according to MFP algorithms, passes through F
(u) functional value produces ranking result, and its F (u) calculation formula is：F (u)=w1*S(u)+w2*B(u)+w3* T (u), MFP are calculated
The specific steps of method are as shown in Figure 4：1. data prediction；2. analyze more architectural features and calculate architectural feature S (u), behavior spy
Levy B (u), theme feature T (u) value；3. the weight w of different characteristic is calculated according to entropy assessmentj；4. by weight wjAnd S (u), T
(u), B (u) value substitutes into formula and carries out F (u) iterative calculation；5. according to F (u) functional values, ranking fruit, its F (u) calculating are produced
Formula is：F (u)=w1*S(u)+w2*B(u)+w3*T(u)。
Embodiment：
First, experimental situation and data
Experimental data set is based on the data captured to forum of Sina, and physical culture forum of main selection Sina is real example data
Source.Forum of Sina is the comprehensive BBS of internet most popularity, possesses maximum core user group；It is the online of most main flow
One of social network-i i-platform.Because our target is to collect the information on relation between user, model and user to opinion
Leader is ranked up, so we are only to the interaction between two users, i.e., following behavior or refers to behavior to connect two use
Family.
For the correctness and accuracy of verification algorithm, data are captured by manual method and obtained by data prediction
Final data set, the concrete condition of data set are as shown in table 1.
Table 1：Experimental data counts basic condition
2nd, algorithm performs result
By analyzing data network, by the MFP algorithms to proposition and classical PageRank (abbreviation PR),
Spend centrality (abbreviation DC), betweenness center (abbreviation BC) and carry out test and comparison close to centrality (abbreviation CC).Draw
Top10 rankings are as shown in table 2：
(1) traditional CENTER ALGORITHM has its limitation, uniquely relies on single feature and is evaluated.For example, degree centrality
The influence of node is weighed by calculating the quantity of follower.However, corpse powder consumingly affect the audio visual effect of people.
Therefore, the user for possessing a large amount of followers may not have a great impact.For example, the user that ID is 4562037183 has largely
Sluggish follower, it is likely to corpse powder.
(2) although it will be seen that the order of ten nodes is different before MFP with PageRank algorithms, it is proposed that
Algorithm preceding ten sequence nodes in only 3 nodes not in the sequence of PageRank algorithms.Therefore, it is important for node
Property analysis, both algorithms can be regarded as consistent.However, the result of MFP algorithms more meets the reality for considering multiple features
Situation.For example, the user that ID is 1305600712 is in MFP algorithms ranking the 8th, and it is number three in PageRank algorithms.
Because according to structure characteristic analysis, its grade is relatively low, reduces the ranking in MFR algorithms, but an overall ranking of is
Still in preceding ten.
3rd, Kendall's coefficient and Duplication
By performing MFP algorithms and classical PR, after DC, BC and CC algorithm, operation result is obtained.100 before choosing first
Name user, correlation calculations are carried out with the result of four kinds of baseline algorithms respectively to the results of MFP algorithms and obtain Ken Deer phase relations
Number.As a result as shown in Figure 5, preceding 100 user's ranked lists of MFP algorithms have very strong correlation with PageRank algorithms,
And weaker correlation between centrad be present.
Finally, the Duplication of preceding 100 users between MFP algorithms and PR, DC, BC and CC baseline algorithm is compared.As a result
It will be appreciated from fig. 6 that with the increase of selected leader of opinion's number, Duplication gradually increases, and finally keeps stable.It is prior
It is that can detect the leader of opinion largely paid high attention to, and non-overlapped part shows that MFP algorithms can find other calculations
The easy ignored node of method.
It is complete by above-mentioned description, relevant staff using the above-mentioned desirable embodiment according to the present invention as enlightenment
Various changes and amendments can be carried out without departing from the scope of the technological thought of the present invention' entirely.The technology of this invention
Property scope is not limited to the content on specification, it is necessary to determines its technical scope according to right.
Claims (4)
1. a kind of social network opinion leader sort algorithm based on multidimensional characteristic analysis, it is characterised in that specific method is as follows：
(1) by being analyzed from three structure, behavior, theme aspects the feature of user, wherein architectural feature S (u) meters
Calculating formula is：S (u)=(ufollowing+ureminding+ubetweenness)/3, behavioural characteristic B (u) calculation formula are：B (u)=
(uactivity+uspread)/2, theme feature T (u) calculation formula are：Wherein ufollowingFor with
With influence power, uremindingTo refer to influence power, uconnectionTo contact influence power, uactivityTo enliven influence power, uspreadFor
Propagating influence, topic (p) are user's theme feature；
(2) the weight w of different characteristic is calculated using entropy assessmentj, calculating process is：
A, the proportion p of j-th of feature under i-th of sample is calculatedij：
Wherein rijThe value of j-th of feature under i-th of sample is expressed as,
B, the comentropy e of j-th of feature is calculatedj：
M indicates m sample in formula,
C, the weight w of j-th of feature is calculatedj：
<mrow>
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<mi>w</mi>
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(3) S (u), B (u) and T (u) characteristic value and the weight w that (2) are drawn are drawn with reference to (1)j, F is calculated according to MFP algorithms
(u) value of function, ranking result is produced by F (u) functional values, its F (u) calculation formula is：F (u)=w1*S(u)+w2*B
(u)+w3*T(u)。
2. a kind of social network opinion leader sort algorithm based on multidimensional characteristic analysis according to claim 1, it is special
Sign is, described ufollowingFor good friend's number of each user, uremindingFor the bean vermicelli number of each user, uconnectionTo be every
Intermediary's centrad of individual user, uactivityPosted for each user and count, comment on number and forwarding model number, uspreadFor model quilt
Comment number and model are forwarded number, and topic (p) determines by model sum and with the ratio of the cluster belonging to model, i.e.,Wherein CiCluster (being drawn by K-means algorithms) belonging to model, | P | it is model sum.
3. a kind of social network opinion leader sort algorithm based on multidimensional characteristic analysis according to claim 1, it is special
Sign is that the entropy assessment comprises the following steps that：(1) the characteristic development of judgment matrix in social networks；(2) sentence
Disconnected matrix normalization processing, obtains normalizing judgment matrix；(3) the proportion p of each feature is calculatedij；(4) each feature is calculated
Comentropy ej；(5) the weight w of each feature is calculatedj。
4. a kind of social network opinion leader sort algorithm based on multidimensional characteristic analysis according to claim 1, it is special
Sign is that the MFP algorithms comprise the following steps that：(1) data prediction；(2) more architectural features are analyzed and calculate architectural feature S
(u), behavioural characteristic B (u), theme feature T (u) value；(3) the weight w of different characteristic is calculated according to entropy assessmentj；(4) by weight
wjAnd S (u), T (u), B (u) characteristic value substitute into formula and carry out F (u) iterative calculation；(5) according to F (u) functional values, ranking is produced
Fruit.
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| CN111311409B (en) * | 2020-02-13 | 2023-04-07 | 腾讯云计算（北京）有限责任公司 | Target object determination method and device, electronic equipment and storage medium |
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